Skip to main content
SpringerLink
Log in

A Comparative Study of Techniques, Datasets and Performances for Intrusion Detection Systems in IoT

  • Conference paper
  • First Online:
Artificial Intelligence Techniques for Advanced Computing Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 130))

Abstract

IoT Security is the area concerned with safeguarding connected systems. IoT involves the set-up of various integrated devices. Devices are identified with a unique identifier, and provided with the ability to transfer data over the network opens them up to several serious vulnerabilities, if not appropriately protected. An Intrusion Detection and Prevention System (IDPS) plays a crucial part in discovering and preventing numerous attacks entering the network and provide an uncompromised secure system. The sheer volume of the sensors in a system comes with limitations such as interoperability, scalability, and storage, where security algorithms like IDS couldn’t perform well as it requires a huge amount of labelled data for training, to detect intrusions and ascertain new attacks. Fog computing plays a major role with a decentralized architecture allows IoT devices to compute, make decisions, take actions, and push only relevant information to the cloud. Data availability is closer and can act immediately for the sensitive information, which in turn helps the IDS to perform well using Artificial Intelligence algorithm to detect and prevent various attacks. This paper categorizes the existing recent researches in IoT Intrusion Detection systems using artificial intelligence and fog computing architecture in terms of technical constraints.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gartner.com (2019) [Online]. Available: https://www.gartner.com/imagesrv/books/iot/iotEbook_digital.pdf. Accessed 29 Oct 2019

  2. Moustafa N, Choo K, Radwan I, Camtepe S (2019) Outlier Dirichlet mixture mechanism: adversarial statistical learning for anomaly Detection in the fog. IEEE Trans Inf Forensics Secur 14:1975–1987

    Article  Google Scholar 

  3. Hasan M, Islam M, Zarif M, Hashem M (2019) Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet Things 7:100059

    Article  Google Scholar 

  4. Nguyen T, Phan T, Nguyen B, So-In C, Baig Z, Sanguanpong S (2019) SeArch: a collaborative and intelligent NIDS architecture for SDN-based cloud IoT networks. IEEE Access 7:107678–107694

    Article  Google Scholar 

  5. Anthi E, Williams L, Slowinska M, Theodorakopoulos G, Burnap P (2019) A supervised intrusion detection system for smart home IoT devices. IEEE Internet Things J 6:9042–9053

    Article  Google Scholar 

  6. B. Sudqi Khater, A. Abdul Wahab, M. Idris, M. Abdulla Hussain, A. Ahmed Ibrahim, A lightweight perceptron-based intrusion detection system for fog computing. Appl Sci 9 (2019) 178

    Google Scholar 

  7. Alaiz-Moreton H, Aveleira-Mata J, Ondicol-Garcia J, Muñoz-Castañeda A, García I, Benavides C (2019) Multiclass classification procedure for detecting attacks on MQTT-IoT protocol. Complexity 2019:1–11

    Article  Google Scholar 

  8. Chaabouni N, Mosbah M, Zemmari A, Sauvignac C, Faruki P (2019) Network intrusion detection for IoT security based on learning techniques. IEEE Commun Surv Tutor 21:2671–2701

    Article  Google Scholar 

  9. Jan S, Ahmed S, Shakhov V, Koo I (2019) Toward a lightweight intrusion detection system for the internet of things. IEEE Access 7:42450–42471

    Article  Google Scholar 

  10. Wang Y, Meng W, Li W, Li J, Liu W, Xiang Y (2018) A fog-based privacy-preserving approach for distributed signature-based intrusion detection. J Parallel Distrib Comput 122:26–35

    Article  Google Scholar 

  11. Abeshu A, Chilamkurti N (2018) Deep learning: the frontier for distributed attack detection in fog-to-things computing. IEEE Commun Mag 56:169–175

    Article  Google Scholar 

  12. An X, Zhou X, Lü X, Lin F, Yang L (2018) Sample selected extreme learning machine based intrusion detection in fog computing and MEC. Wirel Commun Mobile Comput 2018:1–10

    Article  Google Scholar 

  13. Diro A, Chilamkurti N (2018) Leveraging LSTM networks for attack detection in fog-to-things communications. IEEE Commun Mag 56:124–130

    Article  Google Scholar 

  14. Prabavathy S, Sundarakantham K, Shalinie S (2018) Design of cognitive fog computing for intrusion detection in internet of things. J Commun Netw 20:291–298

    Article  Google Scholar 

  15. Meidan Y, Bohadana M, Mathov Y, Mirsky Y, Shabtai A, Breitenbacher D et al (2018) N-BaIoT—Network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Comput 17:12–22

    Article  Google Scholar 

  16. McDermott C, Majdani F, Petrovski A (2018) Botnet detection in the internet of things using deep learning approaches. In: 2018 international joint conference on neural networks (IJCNN), pp 1–8

    Google Scholar 

  17. Deng L, Li D, Yao X, Cox D, Wang H (2018) Mobile network intrusion detection for IoT system based on transfer learning algorithm. Clust Comput 21:1–16

    Google Scholar 

  18. Amouri A, Alaparthy VT, Morgera SD (2018) Cross layer-based intrusion detection based on network behavior for IoT. In: IEEE 19th wireless and microwave technology conference (WAMICON). IEEE, Sand Key, pp 1–4

    Google Scholar 

  19. Liu L, Xu B, Zhang X, Wu X (2018) An intrusion detection method for internet of things based on suppressed fuzzy clustering. EURASIP J Wirel Commun Netw 1:113

    Article  Google Scholar 

  20. Stephen R, Arockiam L (2017) Intrusion detection system to detect sinkhole attack on RPL protocol in internet of things. Int J Electr Electron Comput Sci 4(4):16–20

    Google Scholar 

  21. Bostani H, Sheikhan M (2017) Hybrid of anomaly-based and specification-based IDS for internet of things using unsupervised OPF based on MapReduce approach. Comput Commun 98:52–71

    Article  Google Scholar 

  22. Fu Y, Yan Z, Cao J, Ousmane K, Cao X (2017) An automata based intrusion detection method for internet of things. Mob Inf Syst 2017:13

    Google Scholar 

  23. Khan ZA, Herrmann P (2017) A trust based distributed intrusion detection mechanism for internet of things. In: 2017 IEEE 31st international conference on advanced information networking and applications (AINA). IEEE, Taipei, pp 1169–1176

    Google Scholar 

  24. Lopez-Martin M, Carro B, Sanchez-Esguevillas A, Lloret J (2017) Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in IoT. Sensors 17:1967

    Article  Google Scholar 

  25. Pajouh H, Javidan R, Khayami R, Dehghantanha A, Choo K (2019) A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks. IEEE Trans Emerg Topics Comput 7:314–323

    Article  Google Scholar 

  26. Le A, Loo J, Chai KK, Aiash M (2016) A specification-based IDS for detecting attacks on RPL-based network topology. Information 7(2):1–19

    Article  Google Scholar 

  27. Bace R, Mell P (2001) Intrusion detections systems, National Institute of Standards and Technology

    Google Scholar 

  28. Hosseinpour F, Vahdani Amoli P, Plosila J, Hämäläinen T, Tenhunen H (2016) An intrusion detection system for fog computing and IoT based logistic systems using a smart data approach. Int J Digital Content Technol Appl 10(5):34–46

    Google Scholar 

  29. Cha H, Yang H, Song Y (2018) A study on the design of fog computing architecture using Sensor networks. Sensors 18(11):3633

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. SRM Institute of Science and Technology, Chennai, India

    Arathi Boyanapalli & A. Shanthini

Authors
  1. Arathi Boyanapalli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Shanthini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Shanthini .

Editor information

Editors and Affiliations

  1. Karunya Institute of Technology and Sciences, Coimbatore, India

    D. Jude Hemanth

  2. SRM Institute of Science and Technology, Chennai, India

    G. Vadivu

  3. SRM Institute of Science and Technology, Chennai, India

    M. Sangeetha

  4. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Boyanapalli, A., Shanthini, A. (2021). A Comparative Study of Techniques, Datasets and Performances for Intrusion Detection Systems in IoT. In: Hemanth, D., Vadivu, G., Sangeetha, M., Balas, V. (eds) Artificial Intelligence Techniques for Advanced Computing Applications. Lecture Notes in Networks and Systems, vol 130. Springer, Singapore. https://doi.org/10.1007/978-981-15-5329-5_22

Download citation

Publish with us

Policies and ethics

Search

Navigation